Running Back and Wide Receiver Gold Mining – Week 4

Welcome to week 4. The graphs below summarize the projections from a variety of sources. This week’s summary includes projections from: CBS: CBS Average, Yahoo Sports, NFL, FOX Sports, NumberFire, FantasySharks, ESPN and FantasyFootballNerd. The data for this article was collected on 09/29/15. For more details on WR gold mining and how to interpret the graphs above, see Chad’s post explaining gold mining.

Standard Scoring League Running Back Roundup

From the graph below notice that:

Eddie Lacy, LeSean McCoy, Rashad Jennings, Alfred Blue and Ryan Mathews are the five players with the largest upside (as measured from their (pseudo)medians). For these players, some projections are placing much higher valuations than others. If you are projected to lose this week by quite a few points and are looking for a risky play that may tip the balance in your favor, these are players to consider.

Latavius Murray, Adrian Peterson, Joseph Randle, DeMarco Murray and Tre Mason are the players with the smallest downside, which suggests that while their median projection might not be great, there is less uncertainty concerning how poorly they may perform. So, if you are likely to win by a lot and want to reduce your downside risk, these players may deserve extra attention.

On the other hand, Arian Foster, Devonta Freeman, Karlos Williams, Chris Johnson and LeSean McCoy are the five players with the largest downside this week. If you are planning on starting them, it may be prudent to investigate why some projections have such low expectations for these players.

Point-per-Reception League Running Back Roundup

From the graph below notice that:

Eddie Lacy, Karlos Williams, LeSean McCoy, Alfred Blue and Ryan Mathews are the five players with the largest upside (as measured from their (pseudo)medians). For these players, some projections are placing much higher valuations than others. If you are projected to lose this week by quite a few points and are looking for a risky play that may tip the balance in your favor, these are players to consider.

Latavius Murray, DeMarco Murray, Joseph Randle, Tre Mason and Theo Riddick are the players with the smallest downside, which suggests that while their median projection might not be great, there is less uncertainty concerning how poorly they may perform. So, if you are likely to win by a lot and want to reduce your downside risk, these players may deserve extra attention.

On the other hand, Devonta Freeman, Arian Foster, Karlos Williams, David Johnson and Alfred Blue are the five players with the largest downside this week. If you are planning on starting them, it may be prudent to investigate why some projections have such low expectations for these players.

Standard Scoring League Wide Receiver Roundup

From the graph below notice that:

Alshon Jeffery, Marques Colston, Mike Wallace, Andre Johnson and Markus Wheaton are the five players with the largest upside (as measured from their (pseudo)medians). For these players, some projections are placing much higher valuations than others. If you are projected to lose this week by quite a few points and are looking for a risky play that may tip the balance in your favor, these are players to consider.

Demaryius Thomas, Keenan Allen, DeSean Jackson, Anquan Boldin and Jermaine Kearse are the players with the smallest downside, which suggests that while their median projection might not be great, there is less uncertainty concerning how poorly they may perform. So, if you are likely to win by a lot and want to reduce your downside risk, these players may deserve extra attention.

On the other hand, Doug Baldwin, Ted Ginn, Travis Benjamin, Andre Johnson and Victor Cruz are the five players with the largest downside this week. If you are planning on starting them, it may be prudent to investigate why some projections have such low expectations for these players.

Point-per-Reception League Wide Receiver Roundup

From the graph below notice that:

Marques Colston, Mike Wallace, Andre Johnson, Markus Wheaton and Cole Beasley are the five players with the largest upside (as measured from their (pseudo)medians). For these players, some projections are placing much higher valuations than others. If you are projected to lose this week by quite a few points and are looking for a risky play that may tip the balance in your favor, these are players to consider.

Calvin Johnson, Brandin Cooks, Golden Tate, Anquan Boldin and Malcom Floyd are the players with the smallest downside, which suggests that while their median projection might not be great, there is less uncertainty concerning how poorly they may perform. So, if you are likely to win by a lot and want to reduce your downside risk, these players may deserve extra attention.

On the other hand, DeSean Jackson, Doug Baldwin, Andre Johnson, Victor Cruz and Cole Beasley are the five players with the largest downside this week. If you are planning on starting them, it may be prudent to investigate why some projections have such low expectations for these players.

How do you download a custom projection from the FFA App? The download drop down has 2 options “custom rankings” and “Raw Projections” that download the same file no matter what settings you change in the “change data settings”. Am I missing something? I am trying to export the data that I am seeing in the app.

Works for me, just change the data settings and the “Custom Rankings” file has different columns than the “Raw Projections” file that are customized to your league settings. If you can’t get it to work, let us know what you’re changing, and what you see for each download.

Hey, first I want to say your site is pretty amazing. So keep up the awesome posts. Do you guys have the updated R files that pull the weekly projections from all these sources in the repo? All I see is a file about a year old with CBS, ESPN, and I think one other source. Sorry if I am over looking something.

I just found this website. This is awesome man Good work. I really hope you keep up with the posts. I am very interested in learning R now. Been doing most of this in excel. Keep doing what you’re doing.